Enhanced cervical precancerous lesions detection and classification using Archimedes Optimization Algorithm with transfer learning

Cervical cancer (CC) ranks as the fourth most common form of cancer affecting women, manifesting in the cervix. CC is caused by the Human papillomavirus (HPV) infection and is eradicated by vaccinating women from an early age. However, limited medical facilities present a significant challenge in mid- or low-income countries. It can improve the survivability rate and be successfully treated if the CC is detected at earlier stages. Current technological improvements allow for cost-effective, more sensitive, and rapid screening and treatment measures for CC. DL techniques are widely adopted for the automated detection of CC. DL techniques and architectures are used to detect CC and provide higher detection performance. This study offers the design of Enhanced Cervical Precancerous Lesions Detection and Classification using the Archimedes Optimization Algorithm with Transfer Learning (CPLDC-AOATL) algorithm. The CPLDC-AOATL algorithm aims to diagnose cervical cancer using medical images. At the preliminary stage, the CPLDC-AOATL technique involves a bilateral filtering (BF) technique to eliminate the noise in the input images. Besides, the CPLDC-AOATL technique applies the Inception-ResNetv2 model for the feature extraction process, and the use of AOA chose the hyperparameters. The CPLDC-AOATL technique involves a bidirectional long short-term memory (BiLSTM) model for the cancer detection process. The experimental outcome of the CPLDC-AOATL technique emphasized the superior accuracy outcome of 99.53% over other existing approaches under a benchmark dataset.

• The CPLDC-AOATL model presents a bilateral filtering (BF) technique for efficiently removing noise from medical images, enhancing the quality of input data for subsequent evaluation.
• By implementing the Inception-ResNetv2 technique, the CPLDC-AOATL model implements a state-of- the-art deep learning model for feature extraction, enabling the capture of complex patterns and structures associated with detecting cervical cancer.• The methodology's hyperparameters are carefully selected using the AOA technique, ensuring optimal accom- plishment and generalization capability across diverse datasets and scenarios.• By integrating a bidirectional long-short-term memory (BiLSTM) technique, the CPLDC-AOATL technique achieves superior accuracy in recognizing cervical cancer from medical images, outperforming existing techniques on benchmark datasets.
The remaining sections of the article are arranged as follows: "Literature review" section offers the literature review, and "The proposed method" section represents the proposed method.Then, "Performance validation" section elaborates on the results evaluation, and "Conclusion" section completes the work.

Literature review
Nour et al. 13 introduced a Computer Aided CC Diagnosis employing the Gazelle Optimizer Algorithm with DL (CACCD-GOADL) method.This technique deployed an enriched MobileNetv3 architecture for extraction.Moreover, the method develops an innovative GOA for the tuning process of the enhanced MobileNetv3 model.The method employs a stacked ELM (SELM) technique for classification.Tekchandani et al. 14 developed the DL-based innovative and modified model dependent upon attention mechanism and residual theory with the base UNet architecture for the CLNs detection part (LNdtnNet) of the CADx model.Jeyshri and Kowsigan 15 projected a method for segmenting multi-class cells into Nucleus and Cytoplasmic regions.A multi-resolution U-Net (MRU-Net) system was offered.Primarily, added semantic data was mined employing a series of recurrent convolutions.Secondarily, a convolutional module with different receptive domains was employed.The impact of network width unpredictability can be alleviated by incorporating a convolution layer with many corresponding domains.He et al. 16 developed RMIL, an innovative registration-improved manifold instance learning pipeline.This can be needed only slide-level annotations and a smaller number of patch-level annotations for training.Moreover, self-supervised learning and attention mechanisms have been presented to improve feature extraction.In 17 , Raman spectroscopy was employed.CNN and ResNet classification methods could be made for classification.The attention mechanism squeeze-and-excitation network (SENet) and effective channel attention network (ECANet) units have been incorporated with the developed CNN and ResNet architectures.In 18 , DL methods are utilized.Then, an integrative method with DL techniques and ensemble methods like the maximal occurrence and possibility rate of cervical cells have been developed.The multi-cell analysis of the Pap smear image permitted combined forecasts of CC images employing the developed technique.The authors 19 implemented an intelligent DCNN for CC detection and classification (IDCNN-CDC) system encompassing four primary techniques.The Gaussian filter (GF) and Tsallis entropy method with the dragonfly optimizer (TE-DFO) method are used for noise removel and segmentation.
The cell images have been provided to the DL-based SqueezeNet system for extraction.The authors 20 projected a CAD for CC Screening employing Monarch Butterfly optimizer with DL (CADCCSMBODL) technique, which utilizes transfer learning with tuning approaches for classification.Adaptive filtering (AF) was also deployed with saliency-based segmentation techniques.Also, the method utilizes EfficientNet with the MBO method for parameter optimization.In conclusion, the XGBoost algorithm was implemented for categorization and detection.Senthilkumar et al. 21accumulated the Recurrent lncRNA gene expression data, missing data imputed employing the Mode and Mean Missing method (MMM-DI), and the Hilbert-Schmidt independence criterion with Diversity Artificial Fish Swarm (HSDAFS) model is utilized for the feature selection process.The ENSemble Classification Framework (ENSCF) model is employed for recurrence prediction.Seyala and Abdullah 22 utilized cluster evaluation, employing nonparametric cubic B-spline and penalization methods such as concave penalization function, cubic spline penalty (CSP), and nonparametric pairwise grouping (NPG) techniques.Also, Bayesi, an Information Criteria (BIC) and alternative direction methods of the multiplier (ADMM) models are utilized.In 23 , the authors employed novel methodologies for transfer learning by using uncategorized medical images of the same ailments to mitigate the ImageNet impact.Mukhlif, Al-Khateeb, and Mohammed 24

Image preprocessing
At the preliminary stage, the CPLDC-AOATL technique involves the BF technique to eliminate the noise in the input images.BF is a low-pass filter that smoothens an image by keeping the quality of the object's edge 29 .The reliability and effectiveness of filter in decreasing speckle: (1) where f (q) implies the novel image, h(q) denotes the filtered image, Q(q) indicates the measure of the neighbour- hood window, and ε ′ refers to the pixel location.(ε ′ , q) and (ε ′ ), f (q) , correspondingly, determined as where σ s and σ c are the standard deviation (SD) of the Gaussian random and the ϕ window area.Consequently, Inception-ResNetV2 has captured dynamic features representing an extensive range of images.This model requires an input image size of 299x299x3 .The dropout rate is set as 0.8.This model is finetuned based on the FC layer.The FC layer comprises 1000 object classes; the output class is four for all datasets.Thus, a new FC layer is built and detached from the preceding FC layer, which is later interconnected to the early one.The trained model is used to extract deep features.The activation can be applied, and 1536 deep features are extracted for all the images.The extracting feature was analyzed, and a few redundant data were found.

Inception-ResNetv2 model
(2) At this stage, the hyperparameters are chosen using AOA.AOA is an optimization method derived from the Archimedes' Principle.An object is absorbed in a liquid and pushed upward by a buoyancy force 31 .This force will be equal to the mass of the dislocated liquid.As reported by this method, each object dipped in yn a mican attempts in the equilibrium condition.At this condition, the buoyant force and weight of the object become equal.It will be specified in Eq. ( 5).
Here, 0 denotes the immersed object, v represents the volume, p denotes the density, b specifies fluid, and a represents the acceleration.In AOA, immersed objects produce a population.Primary search will be executed with random values that can be standard in most optimizer methods.Each iterative volume and density value has been upgraded until the method's end conditions are satisfied.Steps are applied in AOA as given below: Step 1: Values of the objects have been arbitrarily allocated as in Eq. ( 6).Now, O i refers to the ith object in N , N refers to population, ub i describes the upper boundary, and lb i refers to the lower boundary.Density (den) and volume (vol) values have been arbitrarily initialized as in Eq. (7).Acceleration (acc i ) will be represented in Eq. ( 8).
Step 2: Density and volume can be upgraded by Eq. ( 9).
On the other hand, den best and vol best describe the best density and volume values.
Step 3: The transfer operator (TF) has been improved.Alternatively, the density factor will be reduced.This allows the exchange among steps (exploration-exploitation) with equilibrium conditions next to the collisions.It can be achieved by the Eq. ( 10).Now, r max denotes the maximal number of iterations.t specifies the iteration number.Density reduc- ing factor (d) reduces over time by applying Eq. ( 11): Step 4 Exploration stage: During the fourth step, collisions arise based on the TF value.Equation ( 12) will upgrade the object's acceleration (acc i ).
Vol i refers to volume, and den i means density.acc i denotes acceleration of object i, and mr pointed out values of random objects.
Step 5 Exploitation stage: Based on the TF values, collisions do not occur.At this condition, Eq. ( 13) will upgrade the object's acceleration.
Now, acc best describes the best acceleration values.
Step 6 Normalize acceleration step: During the 6th stage, acceleration can be extreme in settings, but the performance is distant in the global minimal and reduces over time in alternative conditions.Hence, acc t+1 i,norm is adjusted to the step size variation for every object.Equation ( 14) will be applied.
(5) and represented in Eq. ( 15) whereas C 1 will be equal to 2. When TF is higher than 0.5, the exploration stage will be performed.Object loca- tions have been upgraded by applying Eq. ( 16).
Step 8 Evaluation step.The fitness function (FF) has been calculated.Once a higher result has been determined, it will be remembered.
The AOA develops an FF to realize more excellent classifier solutions.It expresses a positive integer to imply the optimum efficiency of the candidate results.During this case, the decreasing of the classifier error values can be assumed to be FF, as defined in Eq. ( 18).

Cancer detection using BiLSTM
Finally, the CPLDC-AOATL technique involves the BiLSTM model for the cancer detection process.The Bi-LSTM depends on a DL model mainly intended to evaluate the network on numerous multipaths 32 .This model plays a vital part in each component in an input signal, which unites the related facts from both the past and present.In this situation, it makes numerous sufficient outputs.The linear DL model fd(si, vx) = hd bc=1 si bc vx bc , whereas the input is called hd , the terms fd and vx signify the output and weights of the system.The Bi-LSTM model has dual layers of LSTM on side-to-side arrays.The one layer in LSTM was trained beside the input series in the forward direction.The input series was assumed to be in the inverse order for training the extra LSTM layer in a backward direction direction.The LSTM system was measured to correct the gradient issue in RNNs about the more extended sequence data.It has four gates that are set in Eqs. ( 19), ( 20), ( 21) and (22).
Here, R vr , R rv , R ti , R ho denotes the weight matrices on the input condition da mn .Likewise, the weighted metrics from the preceding short-term gd mn−1 are assumed as V vr , V rv , V ti , andV ho .Here, the variables ha vr , ha rv , ha ri , hah 0 are specified as bias.The present long-term condition from the network cd mn is resultant as in Eq. (23)   Lastly, the output f mn is resultant in Eq. (24)   The variable cd mn−1 is stated as a preceding long-term condition.( 15) ) rv mn = φ R rv da mn + V rv gd mn−1 + ha rv (20)  vr mn = tanh R vr da mn + V vr gd mn−1 + ha vr (21)  ti mn = ν R ti da mn + V ti gd mn−1 + ha ti (22)  ho mn = ν R ho da mn + V ho gd mn−1 + haho (23) cd mn = rv mn ⊗ cd mn−1 + ti mn ⊗ vr mn (24)  yf mn = gd mn = ho mn ⊗ tanh(cd)
The cancer recognition output of the CPLDC-AOATL technique at 80%TRAPH and 20%TESPH is made in Table 1 and Fig. 5.The obtained values state that the CPLDC-AOATL technique reaches effectual performance.With 80%TRAPH, the CPLDC-AOATL technique gains an average accu y of 98.48%, prec n of 95.15%, reca l of 94.36%, F score of 94.67%, and MCC of 93.81%.Also, based on 20%TESPH, the CPLDC-AOATL method acquires an average accu y of 99.53%, prec n of 98.09%, reca l of 98.33%, F score of 98.19%, and MCC of 97.93%, respectively.
The efficiency of the CPLDC-AOATL method on 80%TRAPH/20%TESPH is demonstrated in Fig. 7 3 and Fig. 11 11 .These experiment outcome values indicate that the ShuffleNet and DenseNet systems have the lowest performance.Along with that, the Mor-27 and ResNet-101 techniques have reported slightly boosted results.Moreover, the CACCD-GOADL, EOEL-PCLCCI, and GCN methods have obtained closer performance.Nevertheless, the CPLDC-AOATL technique demonstrates superior performance with a maximum accu y of 99.53%, prec n of 98.09%, reca l of 98.33%, and F score of 98.19%.Therefore, the CPLDC-AOATL technique can enhance the CC detection process.
present a Dual Transfer Learning (DTL) model.The model also integrated data augmentation for class balance and sample augmentation.Pacal and Kılıcarslan 25 implement advanced DL models, including CNN and vision transformer

For
the feature extraction process, the CPLDC-AOATL technique applies the Inception-ResNetv2 model.Inception-ResNetV2 incorporates the inception model and residual network 30 .The multi-branch structures are one of the reasons for the popularity of the Inception module.In each branch, a group of filters (1 × 1, 3 × 3, 5 × 5, etc.) are integrated utilizing concatenation.The residual module can be prominent for its capability to train deep architecture.The proposed model makes effective use of residual connections.Firstly, over a million images from the ImageNet gathered are utilized for training the Inception-ResNetV2 method.This 824-layer network categorizes data into 1000 classes.Figure 2 demonstrates the infrastructure of the Inception-ResNetV2 model.

Figure 8
illustrates a wide-ranging representation of the training loss (TRLA) and validation loss (VALL) results of the CPLDC-AOATL technique with 80%TRAPH/20%TESPH over distinct epochs.The progressive decreases in TRLA highlight the CPLDC-AOATL technique, increasing the weights and diminishing the classification error under TRA and TES data.The figure specifies a clear understanding of the CPLDC-AOATL system related to the TRA data, highlighting its proficiency in capturing patterns.Significantly, the CPLDC-AOATL model continually raises its parameters to lessen the variances among the prediction and real TRA class labels.Examining the PR curve, as reported in Fig.9, the results ensured that the CPLDC-AOATL technique at 80%TRAPH/20%TESPH gradually achieves boosted PR values in every class.It verifies the improved abilities of the CPLDC-AOATL algorithm in identifying distinct classes, demonstrating proficiency in the recognition of classes.Furthermore, in Fig.10, ROC curves made by the CPLDC-AOATL method on 80%TRAPH/20%TESPH exceeded the classification of distinct labels.It gives a detailed understanding of TPR and FRP tradeoffs over

Table 3 .
Comparative outcomes of the CPLDC-AOATL model with recent existing methods.